pre-semester course notes set up

_quarto.yml

@@ -17,32 +17,32 @@ book:
     chapters:
         - index.md
         - intro_lec/introduction.qmd
-        - pandas_1/pandas_1.qmd
-        - pandas_2/pandas_2.qmd
-        - pandas_3/pandas_3.qmd
-        - eda/eda.qmd
-        - regex/regex.qmd
-        - visualization_1/visualization_1.qmd
-        - visualization_2/visualization_2.qmd
-        - sampling/sampling.qmd
-        - intro_to_modeling/intro_to_modeling.qmd
-        - constant_model_loss_transformations/loss_transformations.qmd
-        - ols/ols.qmd
-        - gradient_descent/gradient_descent.qmd
-        - feature_engineering/feature_engineering.qmd
-        - case_study_HCE/case_study_HCE.qmd
-        - cv_regularization/cv_reg.qmd
-        - probability_1/probability_1.qmd
-        - probability_2/probability_2.qmd
-        - inference_causality/inference_causality.qmd
-        # - case_study_climate/case_study_climate.qmd
-        - sql_I/sql_I.qmd
-        - sql_II/sql_II.qmd
-        - logistic_regression_1/logistic_reg_1.qmd
-        - logistic_regression_2/logistic_reg_2.qmd
-        - pca_1/pca_1.qmd
-        - pca_2/pca_2.qmd
-        - clustering/clustering.qmd
+        # - pandas_1/pandas_1.qmd
+        # - pandas_2/pandas_2.qmd
+        # - pandas_3/pandas_3.qmd
+        # - eda/eda.qmd
+        # - regex/regex.qmd
+        # - visualization_1/visualization_1.qmd
+        # - visualization_2/visualization_2.qmd
+        # - sampling/sampling.qmd
+        # - intro_to_modeling/intro_to_modeling.qmd
+        # - constant_model_loss_transformations/loss_transformations.qmd
+        # - ols/ols.qmd
+        # - gradient_descent/gradient_descent.qmd
+        # - feature_engineering/feature_engineering.qmd
+        # - case_study_HCE/case_study_HCE.qmd
+        # - cv_regularization/cv_reg.qmd
+        # - probability_1/probability_1.qmd
+        # - probability_2/probability_2.qmd
+        # - inference_causality/inference_causality.qmd
+        # # - case_study_climate/case_study_climate.qmd
+        # - sql_I/sql_I.qmd
+        # - sql_II/sql_II.qmd
+        # - logistic_regression_1/logistic_reg_1.qmd
+        # - logistic_regression_2/logistic_reg_2.qmd
+        # - pca_1/pca_1.qmd
+        # - pca_2/pca_2.qmd
+        # - clustering/clustering.qmd
 
     sidebar:
         logo: "data100_logo.png"

index.md

@@ -6,8 +6,8 @@ jupyter: python3
 
 ## About the Course Notes
 
-This text offers supplementary resources to accompany lectures presented in the Summer 2024 Edition of the UC Berkeley course Data 100: Principles and Techniques of Data Science. 
+This text offers supplementary resources to accompany lectures presented in the Fall 2024 Edition of the UC Berkeley course Data 100: Principles and Techniques of Data Science. 
 
-New notes will be added each week to accompany live lectures. See the full calendar of lectures on the [course website](https://ds100.org/su24/). 
+New notes will be added each week to accompany live lectures. See the full calendar of lectures on the [course website](https://ds100.org/fa24/). 
 
-If you spot any typos or would like to suggest any changes, please fill out the [Data 100 Content Feedback Form (Summer 2024)](https://docs.google.com/forms/d/e/1FAIpQLSfDrb4YsIkKHMU6uVlO0zxbAbt5TRylpdmOLetfxxE6t-lI0w/viewform?usp=sf_link). Note that this link will only work if you have an @berkeley.edu email address. If you’re not a student at Berkeley and would like to provide feedback, please email us at **data100.instructors@berkeley.edu**.
+If you spot any typos or would like to suggest any changes, please email us at **data100.instructors@berkeley.edu**.